论文标题
磁导航挑战问题的信号增强
Signal Enhancement for Magnetic Navigation Challenge Problem
论文作者
论文摘要
利用地球的磁场进行导航已显示出有望作为其他导航系统的可行替代方案。磁性导航系统使用磁力计收集自己的磁场数据，并使用磁异常图来确定当前位置。当磁力计的磁场测量不仅包括地球，还包括安装在其上的车辆上时，磁场测量磁场的最大挑战就会出现。很难将地球磁异常场与传感器的总磁场读数分开，这对于导航至关重要。这个挑战问题的目的是将地球和飞机磁信号解散，以得出一个干净的信号来执行磁导航。数据集上的基线测试表明，可以使用机器学习（ML）从总磁场中提取地球磁场。挑战是使用训练有素的模型从总磁场中删除飞机磁场。这项挑战提供了一个机会，可以通过使用科学机器学习（SCIML）方法来构建从数据集中删除飞机磁场的有效模型，该方法由与磁导航物理学集成的ML算法组成。
Harnessing the magnetic field of the Earth for navigation has shown promise as a viable alternative to other navigation systems. A magnetic navigation system collects its own magnetic field data using a magnetometer and uses magnetic anomaly maps to determine the current location. The greatest challenge with magnetic navigation arises when the magnetic field measurements from the magnetometer encompass the magnetic field from not just the Earth, but also from the vehicle on which it is mounted. It is difficult to separate the Earth magnetic anomaly field, which is crucial for navigation, from the total magnetic field reading from the sensor. The purpose of this challenge problem is to decouple the Earth and aircraft magnetic signals in order to derive a clean signal from which to perform magnetic navigation. Baseline testing on the dataset has shown that the Earth magnetic field can be extracted from the total magnetic field using machine learning (ML). The challenge is to remove the aircraft magnetic field from the total magnetic field using a trained model. This challenge offers an opportunity to construct an effective model for removing the aircraft magnetic field from the dataset by using a scientific machine learning (SciML) approach comprised of an ML algorithm integrated with the physics of magnetic navigation.
